1. Field of the Invention
The present invention generally concerns the identification of (i) pathways and (ii) critical points in, and (iii) the generation of mathematical models of, existing and proposed cellular metabolic networks comprised of biochemical reactions or mechanisms with genetic or non-genetic associations.
More specifically, the present invention relates to computational methods and systems for the analysis and modeling of cellular metabolic networks so that, inter alia, potential targets in support of the directed development of therapeutic agents and engineered microbial strains may be identified.
2. Description of the Prior Art
2.1 General Background
Within a cell of any organism there are complicated networks of interacting proteins and enzymes that perform certain chemical conversions and transformations. These conversions and transformations—life processes—ultimately lead to the production of the (i) necessary building blocks (biomass constituents such as amino acids, nucleotides, phospholipids, etc.) and (ii) energy requirements of the cell. Environmental substances are processed to meet the demands of a living cell through this the cell's network of biochemical reactions.
These biochemical reaction networks primarily involve the use of enzymes derived from particular genes whose chromosomal location and function have been characterized, as well as enzymes inferred to be present based on similarity of their genomic sequence to the genomic sequences of enzyme-coding genes in other organisms. There is presently, circa 2000, much focus on attempting to model and to ‘reconstruct’ these networks of a living organism based, primarily, on the use of genome sequence information of the organism.
Meanwhile, the arsenal of reactions that a cell has at its disposal dictate the production capabilities and maximal performance characteristics of the cell. To change these capabilities the cell would have to acquire new biochemical reactions through some evolutionary mechanism. In so doing the cell would of necessity increase the range of feasible routes by which it could meet certain cellular demands from a set of environmental supplies.
2.2 The Utility of Mathematics to Analyze Biochemical Reaction Networks
The capabilities of cellular biochemical reaction networks (i) to produce necessary building blocks and energy requirements, and (ii) to evolve the reaction pathways by which cellular production(s) is (are) realized, can be comprehensively examined using rigorous mathematics. Mathematical examination yields results which are biochemically meaningful, serving to predict the performance of the biochemical reaction network.
There exists one particular type of mathematical analysis of cellular biochemical reaction networks called “convex analysis”. need definition. Some of the principles of convex analysis were previously used by Schuster to find “elementary nodes”, or reactions within the biochemical reaction networks. See Schuster, S., T. Dandekar and D. A. Fell, 1999, Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering, Trends Biotechnology 17(2): 53-60. See also Schuster, S. and C. Hilgetag, 1994, On elementary flux modes in biochemical reaction systems at steady state. J. Biological Systems 2(2): 165-182. Finally see Schuster, S., C. Hilgetag, J. H. Woods and D. A. Fell, 1996, Elementary modes of functioning in biochemical networks.
Convex analysis was also previously used by Bruce Clarke to find “extreme currents”, or reaction pathways through the biochemical reaction networks by which pathways the biochemical reaction networks succeed in processing environmental substances into the building blocks and energy requirements of the cell. See Clarke, B. L., 1980, Stability of Complex Reaction Networks. Advances in Chemical Physics 43: 1-215. See also Clarke, B. L., 1981, Complete set of steady states for the general stoichiometric dynamical system. J. Chem. Phys. 75(10): 4970-4979.
The mathematics associated with convex analysis may be used to determine the minimal set of biochemical pathways by which some particular capability of the biochemical reaction network is realized. These pathways satisfy both (i) mass balance constraints (associated with stoichiometry) and (ii) directional constraints placed on reactions (associated with thermodynamics).
These pathways are termed “extreme pathways”, and can beneficially be used to examine the functional capabilities of a biochemical reaction network. Importantly, from knowledge of these extreme pathways it is possible to determine all of the possible combinations of reactions that need to be eliminated from the network to remove some particular capability(ies) of the network. From the lists of reactions it is a simple step to determine the enzymes and genes responsible for these reactions.
Consider now that the elimination of these genes should then render the biochemical reaction network incapable of reaching some particular outcome, some particular demand(s) of the cell!
Conversely, once it is understood what a biochemical reaction network is doing, and how it is doing it, then it may become possible to “re-engineer” the network, and the organism, to steer more of its output into desired channels (i.e., to make more of a desired reaction product).
Mathematical tools that permit recognition of pathways within biochemical reaction networks, and of the genes involved with the reactions within these pathways, have still further implications for the development of antibiotics to combat microbial infections. The tools permit recognition of how a deleterious process and pathway of the biochemical reaction network might be stopped, or at least disrupted.
Alternatively, the same mathematical computational tools can be used to improve the design and engineering of organisms for industrial application such as the production of bio-commodities. The tools permit recognition of how an beneficial process and pathway of the biochemical reaction network might be augmented or accentuated.
2.3 Specific Prior Art Mathematical Analysis of Biochemical Reaction Networks
Convex analysis has been previously used to study biochemical systems and to generate related sets of pathways called elementary modes and extreme currents. For a comprehensive review see the paper by inventor of the present invention Schilling and his colleagues, see Schilling, C. H., S. Schuster, B. O. Palsson and R. Heinrich, 1999, Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnology Progress 15(3): 296-303.
Elementary modes have been used to metabolically engineer bacteria for producing aromatic amino acid precursors at yields near the maximum theoretical yield. See Liao, J. C., S-Y Hou and Y-P Chao, 1996, Pathway Analysis, Engineering, and Physiological Considerations for Redirecting Central Metabolism.
Liao, et al., report research where all of the elementary modes for a reduced reaction network in Escherichia coli were calculated and studied to determine the optimal flux distributions through a central metabolism that redirected carbon flow to the pathways from aromatic amino acid production. Reactions that did not appear in the optimal pathways were considered indispensable, while those that did appear in the optimal pathways were candidates for over-expression.
A similar analysis can be performed with the extreme pathways rather than elementary modes. For the precise difference between these two approaches see Schilling, C. H., D. Letscher and B. O. Palsson, 2000, Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. Journal of Theoretical Biology 203(3): 229-248.
Still other papers and publications discuss the complex analysis of biochemical reaction networks. See (i) Clarke, B. L. 1980, Stability of Complex Reaction Networks, Advances in Chemical Physics 43: 1-215; (ii) Clarke, B. L., 1981, Complete set of steady states for the general stoichiometric dynamical system, J. Chem. Phys. 75(10): 4970-4979; (iii) Edwards, J. S., R. Ramakrishna, C. H. Schilling and B. O. Palsson, 1999, Metabolic flux balance analysis, (iv) Metabolic Engineering, S. Y. Lee and E. T. Papoutsakis, New York, Marcel Decker, Inc.: 13-58; (v) Liao, J. C., S-Y Hou and Y-P Chao, 1996, Pathway Analysis, Engineering, and Physiological Considerations for Redirecting Central Metabolism, Biotechnology and Bioengineering 52: 129-140; (vi) Schilling, C. H., S. Schuster, B. O. Palsson and R. Heinrich, 1999, Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnology Progress 15(3): 296-303; (vii) Schuster, S., T. Dandekar and D. A. Fell, 1999, Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering, Trends Biotechnology 17(2): 53-60; (viii) Schuster, S. and C. Hilgetag, 1994, On elementary flux modes in biochemical reaction systems at steady state, J. Biological Systems 2(2): 165-182; (ix) Schuster, S., C. Hilgetag, J. H. Woods and D. A. Fell, 1996, Elementary modes of functioning in biochemical networks; (x) Computation in Cellular and Molecular Biological Systems, R. Cuthbertson, M. Holcombe and R. Paton, London, World Scientific: 151-165; and (xi) Varma, A. and B. O. Palsson, 1994. Metabolic Flux Balancing: Basic concepts, Scientific and Practical Use. Bio/Technology 12: 994-998.